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Record W4399333206 · doi:10.1080/00084433.2024.2357007

Fundamental comminution efficiency gains with the CAHM platform technology

2024· article· en· W4399333206 on OpenAlexaffabout
Shannon Wilson, M. Mousaviraad, Lawrence Nordell, Robert L. Stephens, Gillian Holcroft

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2024
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsCentre for Excellence in Mining Innovation
Fundersnot available
KeywordsComminutionProcess engineeringMaterials scienceMetallurgyEngineering

Abstract

fetched live from OpenAlex

The Conjugate Anvil Hammer Mill (CAHM) is a platform technology invented by Lawrence Nordell that embodies Dr Klaus Schönert’s guiding principles for highly efficient comminution into a series of commercial, production-scalable devices across multiple product size ranges. Schönert demonstrated that unconfined, compression breakage of single particles (or thin particle beds) between parallel plates produces the most energy efficient particle breakage. The CAHM Mark 1 Prototype and the MonoRoll are 2 new comminution devices that leverage these fundamental principles to produce comparable breakage at a fraction of the energy consumed by SAG mills, Ball mills or even High Pressure Grinding Rolls. The CAHM machine prototype has been tested at feed rates in excess of 60 t/h and is robust enough to be operated at a mine site for a pre-commercial demonstration. The MonoRoll is a 2 t/h retrofit of an existing pilot-scale ball mill. A mining industry consortium led by the Canada Mining Innovation Council and supported by Quebec and Federal government funding has designed, fabricated and tested both prototype machines over the last 4 years. Beyond the dramatic energy savings, implementing these technologies will generate additional benefits from simpler circuits and reduced scope 1, 2 and 3 carbon emissions. This paper reviews the design of these 2 prototypes in the context of Schönert’s fundamental principles for efficient breakage.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.211
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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